code: def get_individual_importances(ebm, X, contributions=None):
      """Returns a dict containing the importances of all EBM terms
  
      The dict will de sorted in descending order w.r.t. the importances
  
      Args:
          ebm: A fitted EBM
          X (numpy array): Samples used to compute the group importance
          contributions (numpy array, optional): Contributions of all terms per X's row
  
      Returns:
         a dict where each entry is in the form 'term_name: term_importance'
      """
      if contributions is None:
          contributions = ebm.eval_terms(X)
  
      dict = {}
      for term in ebm.term_names_:
          dict[term] = compute_group_importance(
              [term], ebm, X, contributions
          )
  
      sorted_dict = {
          k: v
          for k, v in sorted(
              dict.items(), key=lambda item: item[1], reverse=True
          )
      }
      return sorted_dict
  
file_path: interpret/python/interpret-core/interpret/glassbox/_ebm/_research/_group_importance.py
imports: import numpy
  import pandas
  import plotly.express
  from sklearn.utils.validation import check_is_fitted
label_desc: Use image node_id 5 for calling a global function with example usage: get_individual_importances(ebm, X, contributions) and returns: sorted_dict